Violent conflict is one of the most persistent challenges affecting the economic livelihoods and food security of individuals worldwide. Despite the surge in literature studying the impacts and drivers of armed conflict, there remains notable knowledge and methodological gaps, particularly regarding the quality of conflict event data. Using various advanced econometric and statistical techniques, this monograph contributes empirically to this literature by studying three interrelated issues. (i) The impact of violence exposure on radicalization; (ii) the magnitude of selection and veracity biases in media-based conflict event data; and (iii) the significance of incorporating violence in nearby locations in predicting armed conflict onset and escalation. First, evidence from the 2009 war on Gaza shows that individuals who experienced violence directly are less likely, on average, to support radical groups. However, when controlling for past electoral preferences, the results reveal a polarization effect among voters exposed directly to violence. Second, by matching conflict event data from several international and national media sources on the Syrian war, media reports are found to capture less than 10\% of the estimated total number of events in the study period. Moreover, reported events across the sources exhibit a systematic spatial clustering and actor-specific biases. Third, using a grid-level panel dataset, the temporal and spatial dynamics of violence, among other geographic factors, are found to significantly drive both conflict onset and escalation. However, violence in neighbouring grids does not enhance the prediction of armed conflict when using high precision units of analysis. In addition to these main findings, I propose and discuss a novel methodology, namely crowdseeding, for collecting conflict event data which works directly with primary sources on the ground to provide reliable information for researchers and policy-makers alike.